{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/spencerpiston/Dropbox/Jennifer Hochschild intrarace class conflict/Perspectives on Politics submission/Perspectives R&R/Perspectives R&R submitted/Perspectives conditional accept/Perspectives final submission/Perspectives replication/2012 ANES analyses/2012 ANES analyses log file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}11 Dec 2020, 21:02:11

{com}. do "/var/folders/bq/_dz_dmks3fz0py_bxfg8x_sr0000gp/T//SD04452.000000"
{txt}
{com}. *replication files: 2012 anes
. use "/Users/spencerpiston/Dropbox/Data/ANES/ANES 2012/anes2012LATERELEASESept212014.dta", clear
{txt}
{com}. 
. *govt should guarantee jobs
. gen govtguarjobs=guarpr_self
{txt}
{com}. replace govtguarjobs=. if govtguarjobs<1
{txt}(438 real changes made, 438 to missing)

{com}. replace govtguarjobs=((govtguarjobs*(-1))+7)/6
{txt}(4,867 real changes made)

{com}. 
. *spending child care
. gen childcare=fedspend_child
{txt}
{com}. recode childcare -9=. -8=. 1=1 2=0 3=.5
{txt}(childcare: 3707 changes made)

{com}. 
. *govt services (& spending): more or less
. *spsrvpr_ssself
. gen govtserv=spsrvpr_ssself
{txt}
{com}. replace govtserv=. if govtserv<1
{txt}(673 real changes made, 673 to missing)

{com}. replace govtserv=(govtserv-1)/6
{txt}(5,241 real changes made)

{com}. 
. * aid to the poor
. tab fedspend_poor

   {txt}PRE: Federal Budget Spending: aid to {c |}
                               the poor {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                            -9. refused {c |}{res}         16        0.27        0.27
{txt}                        -8. don''t know {c |}{res}         40        0.68        0.95
{txt}                           1. increased {c |}{res}      2,336       39.50       40.45
{txt}                           2. decreased {c |}{res}      1,044       17.65       58.10
{txt}3. [kept about the same/ kept the same] {c |}{res}      2,478       41.90      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      5,914      100.00
{txt}
{com}. gen aidtopoor = fedspend_poor
{txt}
{com}. recode aidtopoor -9/-8=. 1=1 2=0 3=0.5
{txt}(aidtopoor: 3578 changes made)

{com}. label var aidtopoor "1=increased, 0.5=keep same, 0=decrease"
{txt}
{com}. 
. * welfare
. tab fedspend_welfare

  {txt}PRE: Federal Budget Spending: welfare {c |}
                               programs {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                            -9. refused {c |}{res}         15        0.25        0.25
{txt}                        -8. don''t know {c |}{res}         51        0.86        1.12
{txt}                           1. increased {c |}{res}        916       15.49       16.60
{txt}                           2. decreased {c |}{res}      2,531       42.80       59.40
{txt}3. [kept about the same/ kept the same] {c |}{res}      2,401       40.60      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      5,914      100.00
{txt}
{com}. gen welfare = fedspend_welfare
{txt}
{com}. recode welfare -9/-8=. 1=1 3=0.5 2=0
{txt}(welfare: 4998 changes made)

{com}. label var welfare "1=increased, 0.5=keep same, 0=decrease"
{txt}
{com}. 
. *govt provision index
. alpha govtguarjobs childcare govtserv aidtopoor welfare, gen(govtprov) item

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
govtguarjobs{col 14}{c |}{res}{col 16}5476{col 24}+{col 31} 0.6934{col 45} 0.5242{col 59} .0578869{col 73} 0.7925
{txt}childcare{col 14}{c |}{res}{col 16}5851{col 24}+{col 31} 0.7489{col 45} 0.5581{col 59} .0515915{col 73} 0.7842
{txt}govtserv{col 14}{c |}{res}{col 16}5241{col 24}+{col 31} 0.7625{col 45} 0.6417{col 59} .0552802{col 73} 0.7665
{txt}aidtopoor{col 14}{c |}{res}{col 16}5858{col 24}+{col 31} 0.8130{col 45} 0.6601{col 59} .0465337{col 73} 0.7489
{txt}welfare{col 14}{c |}{res}{col 16}5848{col 24}+{col 31} 0.7713{col 45} 0.5940{col 59} .0504163{col 73} 0.7721
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0524054{col 73} 0.8103
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. * white
. tab dem_raceeth

 {txt}PRE: SUMMARY- R race {c |}
  and ethnicity group {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
          -9. refused {c |}{res}         27        0.46        0.46
{txt}      -8. don''t know {c |}{res}          2        0.03        0.49
{txt}1. white non-hispanic {c |}{res}      3,495       59.10       59.59
{txt}2. black non-hispanic {c |}{res}      1,016       17.18       76.77
{txt}          3. hispanic {c |}{res}      1,005       16.99       93.76
{txt}4. other non-hispanic {c |}{res}        369        6.24      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}      5,914      100.00
{txt}
{com}. gen white = dem_raceeth
{txt}
{com}. recode white -9/-8=. 1=1 2=0 3=0 4=0
{txt}(white: 2419 changes made)

{com}. 
. * black
. tab dem_raceeth

 {txt}PRE: SUMMARY- R race {c |}
  and ethnicity group {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
          -9. refused {c |}{res}         27        0.46        0.46
{txt}      -8. don''t know {c |}{res}          2        0.03        0.49
{txt}1. white non-hispanic {c |}{res}      3,495       59.10       59.59
{txt}2. black non-hispanic {c |}{res}      1,016       17.18       76.77
{txt}          3. hispanic {c |}{res}      1,005       16.99       93.76
{txt}4. other non-hispanic {c |}{res}        369        6.24      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}      5,914      100.00
{txt}
{com}. gen black = dem_raceeth
{txt}
{com}. recode black -9/-8=. 1=0 2=1 3=0 4=0
{txt}(black: 5914 changes made)

{com}. 
. * latino
. tab dem_raceeth

 {txt}PRE: SUMMARY- R race {c |}
  and ethnicity group {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
          -9. refused {c |}{res}         27        0.46        0.46
{txt}      -8. don''t know {c |}{res}          2        0.03        0.49
{txt}1. white non-hispanic {c |}{res}      3,495       59.10       59.59
{txt}2. black non-hispanic {c |}{res}      1,016       17.18       76.77
{txt}          3. hispanic {c |}{res}      1,005       16.99       93.76
{txt}4. other non-hispanic {c |}{res}        369        6.24      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}      5,914      100.00
{txt}
{com}. gen latino = dem_raceeth
{txt}
{com}. recode latino -9/-8=. 1=0 2=0 3=1 4=0
{txt}(latino: 5914 changes made)

{com}. 
. *less than high school
. gen lessthanhs=.
{txt}(5,914 missing values generated)

{com}. replace lessthanhs=0 if dem_edugroup==2|dem_edugroup==3|dem_edugroup==4|dem_edugroup==5
{txt}(5,242 real changes made)

{com}. replace lessthanhs=1 if dem_edugroup==1
{txt}(622 real changes made)

{com}. 
. *high school only
. gen hs=0
{txt}
{com}. replace hs=. if dem_edugroup==-9|dem_edugroup==-2
{txt}(50 real changes made, 50 to missing)

{com}. replace hs=1 if dem_edugroup==2
{txt}(1,442 real changes made)

{com}. 
. *some college
. gen sc=0
{txt}
{com}. replace sc=. if dem_edugroup==-9|dem_edugroup==-2 
{txt}(50 real changes made, 50 to missing)

{com}. replace sc=1 if dem_edugroup==3
{txt}(1,972 real changes made)

{com}. 
. *college plus
. gen cp=0
{txt}
{com}. replace cp=. if dem_edugroup==-9|dem_edugroup==-2
{txt}(50 real changes made, 50 to missing)

{com}. replace cp=1 if dem_edugroup==4|dem_edugroup==5 
{txt}(1,828 real changes made)

{com}. 
. *weights
. *rename the weight variable to eliminate underscore
. gen weightfull=weight_full
{txt}
{com}. gen weightftf=weight_ftf
{txt}
{com}. gen weightweb=weight_web
{txt}
{com}. 
. *7 category PID*
. generate pid = pid_x
{txt}
{com}. recode pid -2=.
{txt}(pid: 24 changes made)

{com}. gen pidrep0to1 = (pid-1)/6
{txt}(24 missing values generated)

{com}. 
. * male
. tab gender_respondent

   {txt}SUMMARY: {c |}
  Gender of {c |}
 Respondent {c |}
   for both {c |}
FTF and Web {c |}
      modes {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
    1. male {c |}{res}      2,845       48.11       48.11
{txt}  2. female {c |}{res}      3,069       51.89      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,914      100.00
{txt}
{com}. gen male = gender_respondent
{txt}
{com}. recode male 2=0
{txt}(male: 3069 changes made)

{com}. 
. * age
. tab dem_agegrp_iwdate

  {txt}PRE: SUMMARY- R age on interview date {c |}
                            (age group) {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
-2. missing, birthdate fields left blan {c |}{res}         60        1.01        1.01
{txt}                    01. age group 17-20 {c |}{res}        183        3.09        4.11
{txt}                    02. age group 21-24 {c |}{res}        328        5.55        9.66
{txt}                    03. age group 25-29 {c |}{res}        425        7.19       16.84
{txt}                    04. age group 30-34 {c |}{res}        456        7.71       24.55
{txt}                    05. age group 35-39 {c |}{res}        405        6.85       31.40
{txt}                    06. age group 40-44 {c |}{res}        482        8.15       39.55
{txt}                    07. age group 45-49 {c |}{res}        466        7.88       47.43
{txt}                    08. age group 50-54 {c |}{res}        641       10.84       58.27
{txt}                    09. age group 55-59 {c |}{res}        671       11.35       69.61
{txt}                    10. age group 60-64 {c |}{res}        585        9.89       79.51
{txt}                    11. age group 65-69 {c |}{res}        520        8.79       88.30
{txt}                    12. age group 70-74 {c |}{res}        331        5.60       93.90
{txt}              13. age group 75 or older {c |}{res}        361        6.10      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      5,914      100.00
{txt}
{com}. gen age = dem_agegrp_iwdate
{txt}
{com}. recode age -2=.
{txt}(age: 60 changes made)

{com}. tab age

        {txt}age {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        183        3.13        3.13
{txt}          2 {c |}{res}        328        5.60        8.73
{txt}          3 {c |}{res}        425        7.26       15.99
{txt}          4 {c |}{res}        456        7.79       23.78
{txt}          5 {c |}{res}        405        6.92       30.70
{txt}          6 {c |}{res}        482        8.23       38.93
{txt}          7 {c |}{res}        466        7.96       46.89
{txt}          8 {c |}{res}        641       10.95       57.84
{txt}          9 {c |}{res}        671       11.46       69.30
{txt}         10 {c |}{res}        585        9.99       79.30
{txt}         11 {c |}{res}        520        8.88       88.18
{txt}         12 {c |}{res}        331        5.65       93.83
{txt}         13 {c |}{res}        361        6.17      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,854      100.00
{txt}
{com}. gen age0to1 = (age-1)/12
{txt}(60 missing values generated)

{com}. 
. * south
. *making state variable into string variable*
. encode sample_state, gen(state)
{txt}
{com}. gen south=0
{txt}
{com}. recode south 0=. if state==.
{txt}(south: 0 changes made)

{com}. recode south 0=1 if state==2
{txt}(south: 82 changes made)

{com}. recode south 0=1 if state==3
{txt}(south: 41 changes made)

{com}. recode south 0=1 if state==10
{txt}(south: 420 changes made)

{com}. recode south 0=1 if state==11
{txt}(south: 165 changes made)

{com}. recode south 0=1 if state==19
{txt}(south: 111 changes made)

{com}. recode south 0=1 if state==26
{txt}(south: 38 changes made)

{com}. recode south 0=1 if state==28
{txt}(south: 224 changes made)

{com}. recode south 0=1 if state==41
{txt}(south: 115 changes made)

{com}. recode south 0=1 if state==43
{txt}(south: 105 changes made)

{com}. recode south 0=1 if state==44
{txt}(south: 545 changes made)

{com}. recode south 0=1 if state==46
{txt}(south: 122 changes made)

{com}. 
. * linked fate - white
. gen linkw=link_white
{txt}
{com}. recode linkw -9/-1=. 2=0
{txt}(linkw: 3505 changes made)

{com}. tab linkw link_white, miss

           {txt}{c |}   POST: White R: life be affected by what happens to
           {c |}                         whites
     linkw {c |} -9. refus  -8. don''  -7. delet  -6. not a  -2. missi {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}     1,471 
{txt}         1 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}     2,409 
{txt}         . {c |}{res}        29        133        152        252          2 {txt}{c |}{res}     2,034 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}        29        133        152        252          2 {txt}{c |}{res}     5,914 


           {txt}{c |} POST: White R: life be affected
           {c |}    by what happens to whites
     linkw {c |} -1. inapp     1. yes      2. no {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}         0          0      1,471 {txt}{c |}{res}     1,471 
{txt}         1 {c |}{res}         0      2,409          0 {txt}{c |}{res}     2,409 
{txt}         . {c |}{res}     1,466          0          0 {txt}{c |}{res}     2,034 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}     1,466      2,409      1,471 {txt}{c |}{res}     5,914 

{txt}
{com}. recode linkw 1=1 if link_whiteamt==1
{txt}(linkw: 0 changes made)

{com}. recode linkw 1=.67 if link_whiteamt==2
{txt}(linkw: 1463 changes made)

{com}. recode linkw 1=.33 if link_whiteamt==3|link_whiteamt==-8|link_whiteamt==-9
{txt}(linkw: 359 changes made)

{com}. tab linkw link_white, miss

           {txt}{c |}   POST: White R: life be affected by what happens to
           {c |}                         whites
     linkw {c |} -9. refus  -8. don''  -7. delet  -6. not a  -2. missi {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}     1,471 
{txt}       .33 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       359 
{txt}       .67 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}     1,463 
{txt}         1 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       587 
{txt}         . {c |}{res}        29        133        152        252          2 {txt}{c |}{res}     2,034 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}        29        133        152        252          2 {txt}{c |}{res}     5,914 


           {txt}{c |} POST: White R: life be affected
           {c |}    by what happens to whites
     linkw {c |} -1. inapp     1. yes      2. no {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}         0          0      1,471 {txt}{c |}{res}     1,471 
{txt}       .33 {c |}{res}         0        359          0 {txt}{c |}{res}       359 
{txt}       .67 {c |}{res}         0      1,463          0 {txt}{c |}{res}     1,463 
{txt}         1 {c |}{res}         0        587          0 {txt}{c |}{res}       587 
{txt}         . {c |}{res}     1,466          0          0 {txt}{c |}{res}     2,034 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}     1,466      2,409      1,471 {txt}{c |}{res}     5,914 

{txt}
{com}. 
. *linked fate - hispanic
. gen linkh=link_hisp
{txt}
{com}. recode linkh -9/-1=. 2=0
{txt}(linkh: 5456 changes made)

{com}. tab linkh link_hisp, miss

           {txt}{c |}   POST: Hisp R: life be affected by what happens to
           {c |}                       Hispanics
     linkh {c |} -9. refus  -8. don''  -7. delet  -6. not a  -1. inapp {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       434 
{txt}         1 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       458 
{txt}         . {c |}{res}         3         25        152        252      4,590 {txt}{c |}{res}     5,022 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}         3         25        152        252      4,590 {txt}{c |}{res}     5,914 


           {txt}{c |} POST: Hisp R: life be
           {c |}   affected by what
           {c |} happens to Hispanics
     linkh {c |}    1. yes      2. no {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         0        434 {txt}{c |}{res}       434 
{txt}         1 {c |}{res}       458          0 {txt}{c |}{res}       458 
{txt}         . {c |}{res}         0          0 {txt}{c |}{res}     5,022 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       458        434 {txt}{c |}{res}     5,914 

{txt}
{com}. recode linkh 1=1 if link_hispamt==1
{txt}(linkh: 0 changes made)

{com}. recode linkh 1=.67 if link_hispamt==2
{txt}(linkh: 265 changes made)

{com}. recode linkh 1=.33 if link_hispamt==3|link_hispamt==-8
{txt}(linkh: 66 changes made)

{com}. tab linkh link_hisp, miss

           {txt}{c |}   POST: Hisp R: life be affected by what happens to
           {c |}                       Hispanics
     linkh {c |} -9. refus  -8. don''  -7. delet  -6. not a  -1. inapp {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       434 
{txt}       .33 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}        66 
{txt}       .67 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       265 
{txt}         1 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       127 
{txt}         . {c |}{res}         3         25        152        252      4,590 {txt}{c |}{res}     5,022 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}         3         25        152        252      4,590 {txt}{c |}{res}     5,914 


           {txt}{c |} POST: Hisp R: life be
           {c |}   affected by what
           {c |} happens to Hispanics
     linkh {c |}    1. yes      2. no {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         0        434 {txt}{c |}{res}       434 
{txt}       .33 {c |}{res}        66          0 {txt}{c |}{res}        66 
{txt}       .67 {c |}{res}       265          0 {txt}{c |}{res}       265 
{txt}         1 {c |}{res}       127          0 {txt}{c |}{res}       127 
{txt}         . {c |}{res}         0          0 {txt}{c |}{res}     5,022 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       458        434 {txt}{c |}{res}     5,914 

{txt}
{com}. 
. *linked fate - black
. gen linkb=link_black
{txt}
{com}. recode linkb -9/-1=. 2=0
{txt}(linkb: 5237 changes made)

{com}. tab linkb link_black, miss

           {txt}{c |}   POST: Black R: life be affected by what happens to
           {c |}                         blacks
     linkb {c |} -9. refus  -8. don''  -7. delet  -6. not a  -2. missi {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       360 
{txt}         1 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       677 
{txt}         . {c |}{res}         3         22        152        252          1 {txt}{c |}{res}     4,877 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}         3         22        152        252          1 {txt}{c |}{res}     5,914 


           {txt}{c |} POST: Black R: life be affected
           {c |}    by what happens to blacks
     linkb {c |} -1. inapp     1. yes      2. no {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}         0          0        360 {txt}{c |}{res}       360 
{txt}         1 {c |}{res}         0        677          0 {txt}{c |}{res}       677 
{txt}         . {c |}{res}     4,447          0          0 {txt}{c |}{res}     4,877 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}     4,447        677        360 {txt}{c |}{res}     5,914 

{txt}
{com}. recode linkb 1=1 if link_blackamt==1
{txt}(linkb: 0 changes made)

{com}. recode linkb 1=.67 if link_blackamt==2
{txt}(linkb: 358 changes made)

{com}. recode linkb 1=.33 if link_blackamt==3|link_blackamt==-8
{txt}(linkb: 66 changes made)

{com}. tab linkb link_black, miss

           {txt}{c |}   POST: Black R: life be affected by what happens to
           {c |}                         blacks
     linkb {c |} -9. refus  -8. don''  -7. delet  -6. not a  -2. missi {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       360 
{txt}       .33 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}        66 
{txt}       .67 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       358 
{txt}         1 {c |}{res}         0          0          0          0          0 {txt}{c |}{res}       253 
{txt}         . {c |}{res}         3         22        152        252          1 {txt}{c |}{res}     4,877 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}         3         22        152        252          1 {txt}{c |}{res}     5,914 


           {txt}{c |} POST: Black R: life be affected
           {c |}    by what happens to blacks
     linkb {c |} -1. inapp     1. yes      2. no {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}         0          0        360 {txt}{c |}{res}       360 
{txt}       .33 {c |}{res}         0         66          0 {txt}{c |}{res}        66 
{txt}       .67 {c |}{res}         0        358          0 {txt}{c |}{res}       358 
{txt}         1 {c |}{res}         0        253          0 {txt}{c |}{res}       253 
{txt}         . {c |}{res}     4,447          0          0 {txt}{c |}{res}     4,877 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}     4,447        677        360 {txt}{c |}{res}     5,914 

{txt}
{com}. 
. * income
. tab inc_incgroup_pre

      {txt}PRE: CASI/WEB: SUMARY- Pre family {c |}
    income (see also: incgroup_prepost) {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                            -9. refused {c |}{res}        235        3.97        3.97
{txt}                        -8. don''t know {c |}{res}        124        2.10        6.07
{txt}-2. missing; iwr mistakenly entered ''2 {c |}{res}        161        2.72        8.79
{txt}                       01. under $5,000 {c |}{res}        567        9.59       18.38
{txt}                      02. $5,000-$9,999 {c |}{res}        177        2.99       21.37
{txt}                    03. $10,000-$12,499 {c |}{res}        182        3.08       24.45
{txt}                    04. $12,500-$14,999 {c |}{res}         94        1.59       26.04
{txt}                    05. $15,000-$17,499 {c |}{res}        166        2.81       28.85
{txt}                    06. $17,500-$19,999 {c |}{res}         91        1.54       30.39
{txt}                    07. $20,000-$22,499 {c |}{res}        196        3.31       33.70
{txt}                    08. $22,500-$24,999 {c |}{res}        111        1.88       35.58
{txt}                    09. $25,000-$27,499 {c |}{res}        204        3.45       39.03
{txt}                    10. $27,500-$29,999 {c |}{res}         88        1.49       40.51
{txt}                    11. $30,000-$34,999 {c |}{res}        305        5.16       45.67
{txt}                    12. $35,000-$39,999 {c |}{res}        271        4.58       50.25
{txt}                    13. $40,000-$44,999 {c |}{res}        246        4.16       54.41
{txt}                    14. $45,000-$49,999 {c |}{res}        169        2.86       57.27
{txt}                    15. $50,000-$54,999 {c |}{res}        263        4.45       61.72
{txt}                    16. $55,000-$59,999 {c |}{res}        132        2.23       63.95
{txt}                    17. $60,000-$64,999 {c |}{res}        218        3.69       67.64
{txt}                    18. $65,000-$69,999 {c |}{res}        155        2.62       70.26
{txt}                    19. $70,000-$74,999 {c |}{res}        164        2.77       73.03
{txt}                    20. $75,000-$79,999 {c |}{res}        183        3.09       76.12
{txt}                    21. $80,000-$89,999 {c |}{res}        253        4.28       80.40
{txt}                    22. $90,000-$99,999 {c |}{res}        188        3.18       83.58
{txt}                  23. $100,000-$109,999 {c |}{res}        213        3.60       87.18
{txt}                  24. $110,000-$124,999 {c |}{res}        171        2.89       90.07
{txt}                  25. $125,000-$149,999 {c |}{res}        195        3.30       93.37
{txt}                  26. $150,000-$174,999 {c |}{res}        148        2.50       95.87
{txt}                  27. $175,000-$249,999 {c |}{res}        152        2.57       98.44
{txt}                   28. $250,000 or more {c |}{res}         92        1.56      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      5,914      100.00
{txt}
{com}. gen income =  inc_incgroup_pre
{txt}
{com}. recode income -9/-8=. -2=.
{txt}(income: 520 changes made)

{com}. gen inc0to1 = (income-1)/27
{txt}(520 missing values generated)

{com}. 
. *Online Appendix 4: distribution of government social provision index
. twoway hist govtprov if white==1, ///
>         graphregion( color(white) ) ///
>         percent ///
>         fcolor(gray) lcolor(black) /// 
>         ylabel(, nogrid) ///
>         yscale(titlegap(2) range(0 10)) ///
>         xtitle("") ///
>         title("White Respondents") ///
>         ytitle(Percent of Respondents) ///
>         xscale(titlegap(4)) ///
>         ylabel(0 "0" 5 "5" 10 "10")
{res}{txt}
{com}. twoway hist govtprov if black==1, ///
>         graphregion( color(white) ) ///
>         percent ///
>         fcolor(gray) lcolor(black) /// 
>         ylabel(, nogrid) ///
>         yscale(titlegap(2) range(0 10)) ///
>         xtitle("") ///
>         title("Black Respondents") ///
>         ytitle(Percent of Respondents) ///
>         xscale(titlegap(4)) ///
>         ylabel(0 "0" 5 "5" 10 "10")     
{res}{txt}
{com}. twoway hist govtprov if latino==1, ///
>         graphregion( color(white) ) ///
>         percent ///
>         fcolor(gray) lcolor(black) /// 
>         ylabel(, nogrid) ///
>         yscale(titlegap(2) range(0 10)) ///
>         xtitle("") ///
>         title("Latino Respondents") ///
>         ytitle(Percent of Respondents) ///
>         xscale(titlegap(4)) ///
>         ylabel(0 "0" 5 "5" 10 "10")
{res}{txt}
{com}.         
. *Figure 3
. svyset [pweight=weightfull]

      {txt}pweight:{col 16}{res}weightfull
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. xi: eststo Whites2012: svy, subpop(white): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,852
{txt}{col 1}Number of PSUs{col 20}= {res}    5,852{txt}{col 49}Population size{col 67}={res}  5,840.248
{txt}{col 49}Subpop. no. obs{col 67}={res}      3,462
{txt}{col 49}Subpop. size{col 67}={res} 4,129.5735
{txt}{col 49}Design df{col 67}= {res}     5,851
{txt}{col 49}F({res}   3{txt},{res}   5849{txt}){col 67}= {res}     12.54
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0174

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0417231{col 26}{space 2} .0216457{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-.0841567{col 67}{space 3} .0007105
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0892098{col 26}{space 2} .0213922{col 37}{space 1}   -4.17{col 46}{space 3}0.000{col 54}{space 4}-.1311463{col 67}{space 3}-.0472733
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0991721{col 26}{space 2} .0210933{col 37}{space 1}   -4.70{col 46}{space 3}0.000{col 54}{space 4}-.1405228{col 67}{space 3}-.0578214
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5070305{col 26}{space 2} .0193673{col 37}{space 1}   26.18{col 46}{space 3}0.000{col 54}{space 4} .4690635{col 67}{space 3} .5449975
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. coefplot (Whites2012, label(Whites)), drop(_cons) xline(0) title("White Respondents") xscale(range(-.25 .05)) xlabel(-.25(.05).05)
{res}{txt}
{com}. graph save graph1, replace
{res}{txt}(file graph1.gph saved)

{com}. xi: eststo Blacks2012: svy, subpop(black): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,875
{txt}{col 1}Number of PSUs{col 20}= {res}    5,875{txt}{col 49}Population size{col 67}={res} 5,873.5714
{txt}{col 49}Subpop. no. obs{col 67}={res}      1,006
{txt}{col 49}Subpop. size{col 67}={res} 692.872001
{txt}{col 49}Design df{col 67}= {res}     5,874
{txt}{col 49}F({res}   3{txt},{res}   5872{txt}){col 67}= {res}      5.11
{txt}{col 49}Prob > F{col 67}= {res}    0.0016
{txt}{col 49}R-squared{col 67}= {res}    0.0410

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0255546{col 26}{space 2} .0346268{col 37}{space 1}   -0.74{col 46}{space 3}0.461{col 54}{space 4}-.0934359{col 67}{space 3} .0423266
{txt}{space 10}sc {c |}{col 14}{res}{space 2} -.098138{col 26}{space 2} .0350726{col 37}{space 1}   -2.80{col 46}{space 3}0.005{col 54}{space 4}-.1668932{col 67}{space 3}-.0293827
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0878426{col 26}{space 2} .0369225{col 37}{space 1}   -2.38{col 46}{space 3}0.017{col 54}{space 4}-.1602242{col 67}{space 3}-.0154609
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .732731{col 26}{space 2} .0307001{col 37}{space 1}   23.87{col 46}{space 3}0.000{col 54}{space 4} .6725474{col 67}{space 3} .7929145
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. coefplot (Blacks2012, label(Blacks)), drop(_cons) xline(0) title("Black Respondents") xscale(range(-.25 .05)) xlabel(-.25(.05).05)
{res}{txt}
{com}. graph save graph2, replace
{res}{txt}(file graph2.gph saved)

{com}. xi: eststo Latinos2012: svy, subpop(latino): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,876
{txt}{col 1}Number of PSUs{col 20}= {res}    5,876{txt}{col 49}Population size{col 67}={res} 5,876.6926
{txt}{col 49}Subpop. no. obs{col 67}={res}        996
{txt}{col 49}Subpop. size{col 67}={res} 648.739703
{txt}{col 49}Design df{col 67}= {res}     5,875
{txt}{col 49}F({res}   3{txt},{res}   5873{txt}){col 67}= {res}     12.58
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0731

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0727738{col 26}{space 2} .0257815{col 37}{space 1}   -2.82{col 46}{space 3}0.005{col 54}{space 4} -.123315{col 67}{space 3}-.0222327
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.1409702{col 26}{space 2} .0273072{col 37}{space 1}   -5.16{col 46}{space 3}0.000{col 54}{space 4}-.1945023{col 67}{space 3}-.0874381
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.1786516{col 26}{space 2} .0362994{col 37}{space 1}   -4.92{col 46}{space 3}0.000{col 54}{space 4}-.2498118{col 67}{space 3}-.1074914
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6647689{col 26}{space 2} .0189423{col 37}{space 1}   35.09{col 46}{space 3}0.000{col 54}{space 4}  .627635{col 67}{space 3} .7019028
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. coefplot (Latinos2012, label(Latinos)), drop(_cons) xline(0) title("Latino Respondents") xscale(range(-.25 .05)) xlabel(-.25(.05).05)
{res}{txt}
{com}. graph save graph3, replace
{res}{txt}(file graph3.gph saved)

{com}. graph combine graph1.gph graph2.gph graph3.gph, graphregion( color(white) ) title("")
{res}{txt}
{com}. 
. *Online Appendix 5a: govt provision without controls (figure 3 based on this)
. svyset [pweight=weightfull]

      {txt}pweight:{col 16}{res}weightfull
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. svy, subpop(white): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,852
{txt}{col 1}Number of PSUs{col 20}= {res}    5,852{txt}{col 49}Population size{col 67}={res}  5,840.248
{txt}{col 49}Subpop. no. obs{col 67}={res}      3,462
{txt}{col 49}Subpop. size{col 67}={res} 4,129.5735
{txt}{col 49}Design df{col 67}= {res}     5,851
{txt}{col 49}F({res}   3{txt},{res}   5849{txt}){col 67}= {res}     12.54
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0174

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0417231{col 26}{space 2} .0216457{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-.0841567{col 67}{space 3} .0007105
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0892098{col 26}{space 2} .0213922{col 37}{space 1}   -4.17{col 46}{space 3}0.000{col 54}{space 4}-.1311463{col 67}{space 3}-.0472733
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0991721{col 26}{space 2} .0210933{col 37}{space 1}   -4.70{col 46}{space 3}0.000{col 54}{space 4}-.1405228{col 67}{space 3}-.0578214
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5070305{col 26}{space 2} .0193673{col 37}{space 1}   26.18{col 46}{space 3}0.000{col 54}{space 4} .4690635{col 67}{space 3} .5449975
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(black): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,875
{txt}{col 1}Number of PSUs{col 20}= {res}    5,875{txt}{col 49}Population size{col 67}={res} 5,873.5714
{txt}{col 49}Subpop. no. obs{col 67}={res}      1,006
{txt}{col 49}Subpop. size{col 67}={res} 692.872001
{txt}{col 49}Design df{col 67}= {res}     5,874
{txt}{col 49}F({res}   3{txt},{res}   5872{txt}){col 67}= {res}      5.11
{txt}{col 49}Prob > F{col 67}= {res}    0.0016
{txt}{col 49}R-squared{col 67}= {res}    0.0410

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0255546{col 26}{space 2} .0346268{col 37}{space 1}   -0.74{col 46}{space 3}0.461{col 54}{space 4}-.0934359{col 67}{space 3} .0423266
{txt}{space 10}sc {c |}{col 14}{res}{space 2} -.098138{col 26}{space 2} .0350726{col 37}{space 1}   -2.80{col 46}{space 3}0.005{col 54}{space 4}-.1668932{col 67}{space 3}-.0293827
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0878426{col 26}{space 2} .0369225{col 37}{space 1}   -2.38{col 46}{space 3}0.017{col 54}{space 4}-.1602242{col 67}{space 3}-.0154609
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .732731{col 26}{space 2} .0307001{col 37}{space 1}   23.87{col 46}{space 3}0.000{col 54}{space 4} .6725474{col 67}{space 3} .7929145
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(latino): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,876
{txt}{col 1}Number of PSUs{col 20}= {res}    5,876{txt}{col 49}Population size{col 67}={res} 5,876.6926
{txt}{col 49}Subpop. no. obs{col 67}={res}        996
{txt}{col 49}Subpop. size{col 67}={res} 648.739703
{txt}{col 49}Design df{col 67}= {res}     5,875
{txt}{col 49}F({res}   3{txt},{res}   5873{txt}){col 67}= {res}     12.58
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0731

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0727738{col 26}{space 2} .0257815{col 37}{space 1}   -2.82{col 46}{space 3}0.005{col 54}{space 4} -.123315{col 67}{space 3}-.0222327
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.1409702{col 26}{space 2} .0273072{col 37}{space 1}   -5.16{col 46}{space 3}0.000{col 54}{space 4}-.1945023{col 67}{space 3}-.0874381
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.1786516{col 26}{space 2} .0362994{col 37}{space 1}   -4.92{col 46}{space 3}0.000{col 54}{space 4}-.2498118{col 67}{space 3}-.1074914
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6647689{col 26}{space 2} .0189423{col 37}{space 1}   35.09{col 46}{space 3}0.000{col 54}{space 4}  .627635{col 67}{space 3} .7019028
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Online Appendix 5b: govt provision with controls
. svyset [pweight=weightfull]

      {txt}pweight:{col 16}{res}weightfull
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. svy, subpop(white): reg govtprov hs sc cp pidrep0to1 male age0to1 south
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,818
{txt}{col 1}Number of PSUs{col 20}= {res}    5,818{txt}{col 49}Population size{col 67}={res} 5,787.7307
{txt}{col 49}Subpop. no. obs{col 67}={res}      3,428
{txt}{col 49}Subpop. size{col 67}={res} 4,077.0562
{txt}{col 49}Design df{col 67}= {res}     5,817
{txt}{col 49}F({res}   7{txt},{res}   5811{txt}){col 67}= {res}    126.07
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2735

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0282712{col 26}{space 2} .0200244{col 37}{space 1}   -1.41{col 46}{space 3}0.158{col 54}{space 4}-.0675264{col 67}{space 3}  .010984
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0832686{col 26}{space 2} .0197596{col 37}{space 1}   -4.21{col 46}{space 3}0.000{col 54}{space 4}-.1220048{col 67}{space 3}-.0445325
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0924012{col 26}{space 2} .0192074{col 37}{space 1}   -4.81{col 46}{space 3}0.000{col 54}{space 4} -.130055{col 67}{space 3}-.0547475
{txt}{space 2}pidrep0to1 {c |}{col 14}{res}{space 2}-.3451918{col 26}{space 2} .0125649{col 37}{space 1}  -27.47{col 46}{space 3}0.000{col 54}{space 4}-.3698236{col 67}{space 3}  -.32056
{txt}{space 8}male {c |}{col 14}{res}{space 2}-.0302902{col 26}{space 2} .0089247{col 37}{space 1}   -3.39{col 46}{space 3}0.001{col 54}{space 4}-.0477859{col 67}{space 3}-.0127944
{txt}{space 5}age0to1 {c |}{col 14}{res}{space 2}-.0749764{col 26}{space 2} .0161726{col 37}{space 1}   -4.64{col 46}{space 3}0.000{col 54}{space 4}-.1066808{col 67}{space 3}-.0432721
{txt}{space 7}south {c |}{col 14}{res}{space 2}-.0059615{col 26}{space 2} .0099843{col 37}{space 1}   -0.60{col 46}{space 3}0.550{col 54}{space 4}-.0255343{col 67}{space 3} .0136114
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7412296{col 26}{space 2} .0219112{col 37}{space 1}   33.83{col 46}{space 3}0.000{col 54}{space 4} .6982755{col 67}{space 3} .7841837
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(black): reg govtprov hs sc cp pidrep0to1 male age0to1 south
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,860
{txt}{col 1}Number of PSUs{col 20}= {res}    5,860{txt}{col 49}Population size{col 67}={res}  5,866.549
{txt}{col 49}Subpop. no. obs{col 67}={res}        991
{txt}{col 49}Subpop. size{col 67}={res} 685.849601
{txt}{col 49}Design df{col 67}= {res}     5,859
{txt}{col 49}F({res}   7{txt},{res}   5853{txt}){col 67}= {res}      5.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1279

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0210293{col 26}{space 2} .0356406{col 37}{space 1}   -0.59{col 46}{space 3}0.555{col 54}{space 4} -.090898{col 67}{space 3} .0488394
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0909843{col 26}{space 2} .0349613{col 37}{space 1}   -2.60{col 46}{space 3}0.009{col 54}{space 4}-.1595213{col 67}{space 3}-.0224473
{txt}{space 10}cp {c |}{col 14}{res}{space 2} -.079309{col 26}{space 2} .0374438{col 37}{space 1}   -2.12{col 46}{space 3}0.034{col 54}{space 4}-.1527126{col 67}{space 3}-.0059053
{txt}{space 2}pidrep0to1 {c |}{col 14}{res}{space 2}-.2435613{col 26}{space 2} .0534769{col 37}{space 1}   -4.55{col 46}{space 3}0.000{col 54}{space 4}-.3483957{col 67}{space 3}-.1387269
{txt}{space 8}male {c |}{col 14}{res}{space 2}-.0180358{col 26}{space 2} .0183635{col 37}{space 1}   -0.98{col 46}{space 3}0.326{col 54}{space 4}-.0540349{col 67}{space 3} .0179634
{txt}{space 5}age0to1 {c |}{col 14}{res}{space 2} .0046354{col 26}{space 2} .0353824{col 37}{space 1}    0.13{col 46}{space 3}0.896{col 54}{space 4}-.0647272{col 67}{space 3}  .073998
{txt}{space 7}south {c |}{col 14}{res}{space 2}-.0212017{col 26}{space 2} .0189708{col 37}{space 1}   -1.12{col 46}{space 3}0.264{col 54}{space 4}-.0583915{col 67}{space 3}  .015988
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7818291{col 26}{space 2} .0471832{col 37}{space 1}   16.57{col 46}{space 3}0.000{col 54}{space 4} .6893326{col 67}{space 3} .8743256
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(latino): reg govtprov hs sc cp pidrep0to1 male age0to1 south
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,854
{txt}{col 1}Number of PSUs{col 20}= {res}    5,854{txt}{col 49}Population size{col 67}={res} 5,868.8309
{txt}{col 49}Subpop. no. obs{col 67}={res}        974
{txt}{col 49}Subpop. size{col 67}={res} 640.878003
{txt}{col 49}Design df{col 67}= {res}     5,853
{txt}{col 49}F({res}   7{txt},{res}   5847{txt}){col 67}= {res}     10.19
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1842

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2} -.076756{col 26}{space 2} .0287408{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.1330986{col 67}{space 3}-.0204135
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.1158273{col 26}{space 2} .0286261{col 37}{space 1}   -4.05{col 46}{space 3}0.000{col 54}{space 4} -.171945{col 67}{space 3}-.0597096
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.1700457{col 26}{space 2} .0342332{col 37}{space 1}   -4.97{col 46}{space 3}0.000{col 54}{space 4}-.2371555{col 67}{space 3} -.102936
{txt}{space 2}pidrep0to1 {c |}{col 14}{res}{space 2}-.2332084{col 26}{space 2} .0354217{col 37}{space 1}   -6.58{col 46}{space 3}0.000{col 54}{space 4} -.302648{col 67}{space 3}-.1637688
{txt}{space 8}male {c |}{col 14}{res}{space 2}-.0289622{col 26}{space 2} .0202765{col 37}{space 1}   -1.43{col 46}{space 3}0.153{col 54}{space 4}-.0687116{col 67}{space 3} .0107872
{txt}{space 5}age0to1 {c |}{col 14}{res}{space 2}-.0550577{col 26}{space 2} .0358602{col 37}{space 1}   -1.54{col 46}{space 3}0.125{col 54}{space 4}-.1253569{col 67}{space 3} .0152415
{txt}{space 7}south {c |}{col 14}{res}{space 2} .0076746{col 26}{space 2} .0202355{col 37}{space 1}    0.38{col 46}{space 3}0.705{col 54}{space 4}-.0319943{col 67}{space 3} .0473436
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7723092{col 26}{space 2} .0322996{col 37}{space 1}   23.91{col 46}{space 3}0.000{col 54}{space 4} .7089901{col 67}{space 3} .8356283
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Online Appendix 5c: govt provision face to face only
. svyset [pweight=weightftf]

      {txt}pweight:{col 16}{res}weightftf
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. svy, subpop(if white==1 & mode==1): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,905
{txt}{col 1}Number of PSUs{col 20}= {res}    5,905{txt}{col 49}Population size{col 67}={res} 2,042.6846
{txt}{col 49}Subpop. no. obs{col 67}={res}        909
{txt}{col 49}Subpop. size{col 67}={res} 1,439.2811
{txt}{col 49}Design df{col 67}= {res}     5,904
{txt}{col 49}F({res}   3{txt},{res}   5902{txt}){col 67}= {res}      5.71
{txt}{col 49}Prob > F{col 67}= {res}    0.0007
{txt}{col 49}R-squared{col 67}= {res}    0.0229

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0370024{col 26}{space 2} .0305998{col 37}{space 1}   -1.21{col 46}{space 3}0.227{col 54}{space 4}-.0969892{col 67}{space 3} .0229845
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0629511{col 26}{space 2} .0303376{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-.1224239{col 67}{space 3}-.0034783
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.1086727{col 26}{space 2} .0299576{col 37}{space 1}   -3.63{col 46}{space 3}0.000{col 54}{space 4}-.1674005{col 67}{space 3}-.0499448
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .542894{col 26}{space 2} .0256394{col 37}{space 1}   21.17{col 46}{space 3}0.000{col 54}{space 4} .4926313{col 67}{space 3} .5931567
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(if black==1 & mode==1): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,907
{txt}{col 1}Number of PSUs{col 20}= {res}    5,907{txt}{col 49}Population size{col 67}={res} 2,050.3924
{txt}{col 49}Subpop. no. obs{col 67}={res}        504
{txt}{col 49}Subpop. size{col 67}={res}   240.6673
{txt}{col 49}Design df{col 67}= {res}     5,906
{txt}{col 49}F({res}   3{txt},{res}   5904{txt}){col 67}= {res}      4.05
{txt}{col 49}Prob > F{col 67}= {res}    0.0069
{txt}{col 49}R-squared{col 67}= {res}    0.0479

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0346973{col 26}{space 2}  .035301{col 37}{space 1}   -0.98{col 46}{space 3}0.326{col 54}{space 4}-.1039003{col 67}{space 3} .0345056
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0923132{col 26}{space 2} .0335243{col 37}{space 1}   -2.75{col 46}{space 3}0.006{col 54}{space 4}-.1580331{col 67}{space 3}-.0265934
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.1107415{col 26}{space 2} .0422192{col 37}{space 1}   -2.62{col 46}{space 3}0.009{col 54}{space 4}-.1935065{col 67}{space 3}-.0279765
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7917832{col 26}{space 2} .0290324{col 37}{space 1}   27.27{col 46}{space 3}0.000{col 54}{space 4}  .734869{col 67}{space 3} .8486974
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(if latino==1 & mode==1): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,913
{txt}{col 1}Number of PSUs{col 20}= {res}    5,913{txt}{col 49}Population size{col 67}={res} 2,052.7912
{txt}{col 49}Subpop. no. obs{col 67}={res}        471
{txt}{col 49}Subpop. size{col 67}={res} 221.606401
{txt}{col 49}Design df{col 67}= {res}     5,912
{txt}{col 49}F({res}   3{txt},{res}   5910{txt}){col 67}= {res}      7.78
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1020

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0883555{col 26}{space 2} .0337943{col 37}{space 1}   -2.61{col 46}{space 3}0.009{col 54}{space 4}-.1546047{col 67}{space 3}-.0221062
{txt}{space 10}sc {c |}{col 14}{res}{space 2} -.146645{col 26}{space 2} .0343112{col 37}{space 1}   -4.27{col 46}{space 3}0.000{col 54}{space 4}-.2139075{col 67}{space 3}-.0793825
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.2217134{col 26}{space 2} .0661943{col 37}{space 1}   -3.35{col 46}{space 3}0.001{col 54}{space 4}-.3514784{col 67}{space 3}-.0919485
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7462744{col 26}{space 2} .0250996{col 37}{space 1}   29.73{col 46}{space 3}0.000{col 54}{space 4} .6970701{col 67}{space 3} .7954788
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Online Appendix 5d: govt provision internet only
. svyset [pweight=weightweb]

      {txt}pweight:{col 16}{res}weightweb
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. svy, subpop(if white==1 & mode==2): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,890
{txt}{col 1}Number of PSUs{col 20}= {res}    5,890{txt}{col 49}Population size{col 67}={res} 3,829.4876
{txt}{col 49}Subpop. no. obs{col 67}={res}      2,553
{txt}{col 49}Subpop. size{col 67}={res} 2,690.2924
{txt}{col 49}Design df{col 67}= {res}     5,889
{txt}{col 49}F({res}   3{txt},{res}   5887{txt}){col 67}= {res}      8.30
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0178

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0422678{col 26}{space 2} .0291404{col 37}{space 1}   -1.45{col 46}{space 3}0.147{col 54}{space 4}-.0993938{col 67}{space 3} .0148582
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.1015327{col 26}{space 2} .0286601{col 37}{space 1}   -3.54{col 46}{space 3}0.000{col 54}{space 4} -.157717{col 67}{space 3}-.0453484
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0926929{col 26}{space 2} .0283642{col 37}{space 1}   -3.27{col 46}{space 3}0.001{col 54}{space 4}-.1482971{col 67}{space 3}-.0370887
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4863398{col 26}{space 2}   .02663{col 37}{space 1}   18.26{col 46}{space 3}0.000{col 54}{space 4} .4341353{col 67}{space 3} .5385443
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(if black==1 & mode==2): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,911
{txt}{col 1}Number of PSUs{col 20}= {res}    5,911{txt}{col 49}Population size{col 67}={res} 3,855.1032
{txt}{col 49}Subpop. no. obs{col 67}={res}        502
{txt}{col 49}Subpop. size{col 67}={res} 452.204701
{txt}{col 49}Design df{col 67}= {res}     5,910
{txt}{col 49}F({res}   3{txt},{res}   5908{txt}){col 67}= {res}      3.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0294
{txt}{col 49}R-squared{col 67}= {res}    0.0441

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0288427{col 26}{space 2} .0465297{col 37}{space 1}   -0.62{col 46}{space 3}0.535{col 54}{space 4} -.120058{col 67}{space 3} .0623725
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.1091956{col 26}{space 2}  .047119{col 37}{space 1}   -2.32{col 46}{space 3}0.021{col 54}{space 4}-.2015661{col 67}{space 3}-.0168251
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0782607{col 26}{space 2}   .04891{col 37}{space 1}   -1.60{col 46}{space 3}0.110{col 54}{space 4}-.1741422{col 67}{space 3} .0176209
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7068911{col 26}{space 2} .0409391{col 37}{space 1}   17.27{col 46}{space 3}0.000{col 54}{space 4} .6266355{col 67}{space 3} .7871468
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(if latino==1 & mode==2): reg govtprov hs sc cp
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,906
{txt}{col 1}Number of PSUs{col 20}= {res}    5,906{txt}{col 49}Population size{col 67}={res} 3,855.8256
{txt}{col 49}Subpop. no. obs{col 67}={res}        525
{txt}{col 49}Subpop. size{col 67}={res} 427.133302
{txt}{col 49}Design df{col 67}= {res}     5,905
{txt}{col 49}F({res}   3{txt},{res}   5903{txt}){col 67}= {res}      7.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0001
{txt}{col 49}R-squared{col 67}= {res}    0.0682

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0688982{col 26}{space 2} .0337099{col 37}{space 1}   -2.04{col 46}{space 3}0.041{col 54}{space 4} -.134982{col 67}{space 3}-.0028144
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.1348509{col 26}{space 2} .0352288{col 37}{space 1}   -3.83{col 46}{space 3}0.000{col 54}{space 4}-.2039124{col 67}{space 3}-.0657895
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.1649321{col 26}{space 2} .0405525{col 37}{space 1}   -4.07{col 46}{space 3}0.000{col 54}{space 4}-.2444299{col 67}{space 3}-.0854344
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .625187{col 26}{space 2} .0241914{col 37}{space 1}   25.84{col 46}{space 3}0.000{col 54}{space 4} .5777631{col 67}{space 3}  .672611
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Online Appendix 5e: same as 5b except controlling for linked fate
. svyset [pweight=weightfull]

      {txt}pweight:{col 16}{res}weightfull
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. svy, subpop(white): reg govtprov hs sc cp pidrep0to1 male age0to1 south linkw
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,462
{txt}{col 1}Number of PSUs{col 20}= {res}    5,462{txt}{col 49}Population size{col 67}={res}  5,335.736
{txt}{col 49}Subpop. no. obs{col 67}={res}      3,072
{txt}{col 49}Subpop. size{col 67}={res} 3,625.0615
{txt}{col 49}Design df{col 67}= {res}     5,461
{txt}{col 49}F({res}   8{txt},{res}   5454{txt}){col 67}= {res}     97.92
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2740

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0114233{col 26}{space 2} .0205641{col 37}{space 1}   -0.56{col 46}{space 3}0.579{col 54}{space 4}-.0517372{col 67}{space 3} .0288906
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0645917{col 26}{space 2} .0202968{col 37}{space 1}   -3.18{col 46}{space 3}0.001{col 54}{space 4}-.1043815{col 67}{space 3} -.024802
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0755038{col 26}{space 2} .0198024{col 37}{space 1}   -3.81{col 46}{space 3}0.000{col 54}{space 4}-.1143243{col 67}{space 3}-.0366833
{txt}{space 2}pidrep0to1 {c |}{col 14}{res}{space 2}-.3418798{col 26}{space 2} .0135176{col 37}{space 1}  -25.29{col 46}{space 3}0.000{col 54}{space 4}-.3683796{col 67}{space 3}  -.31538
{txt}{space 8}male {c |}{col 14}{res}{space 2}-.0318475{col 26}{space 2} .0095293{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-.0505287{col 67}{space 3}-.0131663
{txt}{space 5}age0to1 {c |}{col 14}{res}{space 2}-.0696006{col 26}{space 2}  .017415{col 37}{space 1}   -4.00{col 46}{space 3}0.000{col 54}{space 4}-.1037408{col 67}{space 3}-.0354603
{txt}{space 7}south {c |}{col 14}{res}{space 2}-.0027143{col 26}{space 2} .0106397{col 37}{space 1}   -0.26{col 46}{space 3}0.799{col 54}{space 4}-.0235724{col 67}{space 3} .0181438
{txt}{space 7}linkw {c |}{col 14}{res}{space 2}-.0351088{col 26}{space 2} .0132064{col 37}{space 1}   -2.66{col 46}{space 3}0.008{col 54}{space 4}-.0609985{col 67}{space 3}-.0092191
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7332328{col 26}{space 2} .0233917{col 37}{space 1}   31.35{col 46}{space 3}0.000{col 54}{space 4} .6873757{col 67}{space 3} .7790898
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(black): reg govtprov hs sc cp pidrep0to1 male age0to1 south linkb
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,781
{txt}{col 1}Number of PSUs{col 20}= {res}    5,781{txt}{col 49}Population size{col 67}={res} 5,811.0892
{txt}{col 49}Subpop. no. obs{col 67}={res}        912
{txt}{col 49}Subpop. size{col 67}={res}   630.3898
{txt}{col 49}Design df{col 67}= {res}     5,780
{txt}{col 49}F({res}   8{txt},{res}   5773{txt}){col 67}= {res}      5.07
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1439

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2}-.0207778{col 26}{space 2} .0380697{col 37}{space 1}   -0.55{col 46}{space 3}0.585{col 54}{space 4}-.0954087{col 67}{space 3} .0538531
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.0854651{col 26}{space 2} .0377197{col 37}{space 1}   -2.27{col 46}{space 3}0.024{col 54}{space 4}-.1594098{col 67}{space 3}-.0115204
{txt}{space 10}cp {c |}{col 14}{res}{space 2}-.0902407{col 26}{space 2} .0401725{col 37}{space 1}   -2.25{col 46}{space 3}0.025{col 54}{space 4}-.1689939{col 67}{space 3}-.0114874
{txt}{space 2}pidrep0to1 {c |}{col 14}{res}{space 2}-.2311948{col 26}{space 2} .0538443{col 37}{space 1}   -4.29{col 46}{space 3}0.000{col 54}{space 4}-.3367498{col 67}{space 3}-.1256397
{txt}{space 8}male {c |}{col 14}{res}{space 2}-.0198135{col 26}{space 2} .0191231{col 37}{space 1}   -1.04{col 46}{space 3}0.300{col 54}{space 4}-.0573019{col 67}{space 3} .0176749
{txt}{space 5}age0to1 {c |}{col 14}{res}{space 2} .0175332{col 26}{space 2} .0371755{col 37}{space 1}    0.47{col 46}{space 3}0.637{col 54}{space 4}-.0553448{col 67}{space 3} .0904111
{txt}{space 7}south {c |}{col 14}{res}{space 2}-.0202234{col 26}{space 2} .0189237{col 37}{space 1}   -1.07{col 46}{space 3}0.285{col 54}{space 4} -.057321{col 67}{space 3} .0168742
{txt}{space 7}linkb {c |}{col 14}{res}{space 2} .0637649{col 26}{space 2} .0218358{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .0209586{col 67}{space 3} .1065712
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .742561{col 26}{space 2} .0510783{col 37}{space 1}   14.54{col 46}{space 3}0.000{col 54}{space 4} .6424284{col 67}{space 3} .8426937
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy, subpop(latino): reg govtprov hs sc cp pidrep0to1 male age0to1 south linkh
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,748
{txt}{col 1}Number of PSUs{col 20}= {res}    5,748{txt}{col 49}Population size{col 67}={res} 5,796.0616
{txt}{col 49}Subpop. no. obs{col 67}={res}        868
{txt}{col 49}Subpop. size{col 67}={res} 568.108702
{txt}{col 49}Design df{col 67}= {res}     5,747
{txt}{col 49}F({res}   8{txt},{res}   5740{txt}){col 67}= {res}      9.46
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1990

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}hs {c |}{col 14}{res}{space 2} -.076557{col 26}{space 2} .0301264{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.1356162{col 67}{space 3}-.0174979
{txt}{space 10}sc {c |}{col 14}{res}{space 2}-.1040758{col 26}{space 2} .0305373{col 37}{space 1}   -3.41{col 46}{space 3}0.001{col 54}{space 4}-.1639404{col 67}{space 3}-.0442112
{txt}{space 10}cp {c |}{col 14}{res}{space 2} -.158837{col 26}{space 2} .0379107{col 37}{space 1}   -4.19{col 46}{space 3}0.000{col 54}{space 4}-.2331563{col 67}{space 3}-.0845177
{txt}{space 2}pidrep0to1 {c |}{col 14}{res}{space 2}-.2282877{col 26}{space 2} .0389504{col 37}{space 1}   -5.86{col 46}{space 3}0.000{col 54}{space 4} -.304645{col 67}{space 3}-.1519303
{txt}{space 8}male {c |}{col 14}{res}{space 2}-.0331096{col 26}{space 2} .0214116{col 37}{space 1}   -1.55{col 46}{space 3}0.122{col 54}{space 4}-.0750843{col 67}{space 3} .0088651
{txt}{space 5}age0to1 {c |}{col 14}{res}{space 2}-.0433559{col 26}{space 2} .0371444{col 37}{space 1}   -1.17{col 46}{space 3}0.243{col 54}{space 4}-.1161729{col 67}{space 3}  .029461
{txt}{space 7}south {c |}{col 14}{res}{space 2} .0085256{col 26}{space 2} .0212265{col 37}{space 1}    0.40{col 46}{space 3}0.688{col 54}{space 4}-.0330862{col 67}{space 3} .0501375
{txt}{space 7}linkh {c |}{col 14}{res}{space 2} .0808022{col 26}{space 2} .0263108{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .0292232{col 67}{space 3} .1323813
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7266299{col 26}{space 2} .0363079{col 37}{space 1}   20.01{col 46}{space 3}0.000{col 54}{space 4} .6554526{col 67}{space 3} .7978071
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Online Appendix 6: same as Figure 3 except income
. svyset [pweight=weightfull]

      {txt}pweight:{col 16}{res}weightfull
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. xi: eststo Whites2012: svy, subpop(white): reg govtprov inc0to1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,673
{txt}{col 1}Number of PSUs{col 20}= {res}    5,673{txt}{col 49}Population size{col 67}={res} 5,590.8497
{txt}{col 49}Subpop. no. obs{col 67}={res}      3,283
{txt}{col 49}Subpop. size{col 67}={res} 3,880.1752
{txt}{col 49}Design df{col 67}= {res}     5,672
{txt}{col 49}F({res}   1{txt},{res}   5672{txt}){col 67}= {res}    123.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0498

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}inc0to1 {c |}{col 14}{res}{space 2}-.1875314{col 26}{space 2} .0168796{col 37}{space 1}  -11.11{col 46}{space 3}0.000{col 54}{space 4} -.220622{col 67}{space 3}-.1544409
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5324565{col 26}{space 2} .0101013{col 37}{space 1}   52.71{col 46}{space 3}0.000{col 54}{space 4}  .512654{col 67}{space 3} .5522589
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xi: eststo Blacks2012: svy, subpop(black): reg govtprov inc0to1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,748
{txt}{col 1}Number of PSUs{col 20}= {res}    5,748{txt}{col 49}Population size{col 67}={res} 5,790.4865
{txt}{col 49}Subpop. no. obs{col 67}={res}        879
{txt}{col 49}Subpop. size{col 67}={res} 609.787101
{txt}{col 49}Design df{col 67}= {res}     5,747
{txt}{col 49}F({res}   1{txt},{res}   5747{txt}){col 67}= {res}     10.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0010
{txt}{col 49}R-squared{col 67}= {res}    0.0429

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}inc0to1 {c |}{col 14}{res}{space 2}-.1393069{col 26}{space 2} .0423213{col 37}{space 1}   -3.29{col 46}{space 3}0.001{col 54}{space 4}-.2222726{col 67}{space 3}-.0563412
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7173958{col 26}{space 2} .0168827{col 37}{space 1}   42.49{col 46}{space 3}0.000{col 54}{space 4} .6842993{col 67}{space 3} .7504923
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xi: eststo Latinos2012: svy, subpop(latino): reg govtprov inc0to1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     5,779
{txt}{col 1}Number of PSUs{col 20}= {res}    5,779{txt}{col 49}Population size{col 67}={res} 5,828.2866
{txt}{col 49}Subpop. no. obs{col 67}={res}        899
{txt}{col 49}Subpop. size{col 67}={res} 600.333702
{txt}{col 49}Design df{col 67}= {res}     5,778
{txt}{col 49}F({res}   1{txt},{res}   5778{txt}){col 67}= {res}     44.12
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1124

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}    govtprov{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}inc0to1 {c |}{col 14}{res}{space 2}-.2712353{col 26}{space 2} .0408324{col 37}{space 1}   -6.64{col 46}{space 3}0.000{col 54}{space 4}-.3512821{col 67}{space 3}-.1911884
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6719987{col 26}{space 2} .0173708{col 37}{space 1}   38.69{col 46}{space 3}0.000{col 54}{space 4} .6379454{col 67}{space 3} .7060521
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. coefplot (Whites2012, label(Whites)) (Blacks2012, label(Blacks)) (Latinos2012, label(Latinos)), drop(_cons) xline(0) title("")
{res}{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/spencerpiston/Dropbox/Jennifer Hochschild intrarace class conflict/Perspectives on Politics submission/Perspectives R&R/Perspectives R&R submitted/Perspectives conditional accept/Perspectives final submission/Perspectives replication/2012 ANES analyses/2012 ANES analyses log file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Dec 2020, 21:03:11
{txt}{.-}
{smcl}
{txt}{sf}{ul off}